Evidence map›Paper›PMID 38061463›Full record

ReviewBiological psychiatry2024

Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions.

Moritz Dannhauer, Luis J Gomez, Pei L Robins, Dezhi Wang, Nahian I Hasan, Axel Thielscher, Hartwig R Siebner, Yong Fan, Zhi-De Deng

Open access · greenAbstract readReview
In one paragraph

Review in Biological psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 35 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 2 countries.

Moritz DannhauerComputational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland.
Luis J GomezSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.
Pei L RobinsComputational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland.
Dezhi WangSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.
Nahian I HasanSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.
Axel ThielscherDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
Hartwig R SiebnerDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark; Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Yong FanDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Zhi-De DengComputational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland. Electronic address: zhi-de.deng@nih.gov.
National Institute of Mental Health · USPurdue University West Lafayette · USHvidovre Hospital · DKUniversity of Copenhagen · DKUniversity of Pennsylvania · US

Funding

Non-invasive Neuromodulation Unit (NNU)ZIAMH002955 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI ZARATE, CARLOS · 2016 to 2025
$40.3M
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in YouthR01EB022573 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yong Fan, Theodore Satterthwaite · 2016 to 2026
$6.0M
The Neuroimaging Brain Chart Software SuiteU24NS130411 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Christos Davatzikos, Yong Fan · 2023 to 2026
$3.7M
Individualized Closed Loop TMS for Working Memory EnhancementR01MH120811 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI FAN, YONG, OATHES, DESMOND · 2019 to 2023
$3.5M
Fast and robust deep learning tools for analysis of neuroimaging data of Alzheimer's diseaseR01AG066650 · NIA · UNIVERSITY OF PENNSYLVANIA · PI FAN, YONG · 2021 to 2025
$3.4M
Accurate and reliable computational dosimetry and targeting for transcranial magnetic stimulationR00MH120046 · NIMH · PURDUE UNIVERSITY · PI GOMEZ, LUIS JAVIER · 2020 to 2022
$676k
Intramural NIH HHS ZIA MH002955NIBIB NIH HHS R01 EB022573NIMH NIH HHS R00 MH120046NIMH NIH HHS R01 MH120811NINDS NIH HHS U24 NS130411
6 · The paper itself

Abstract

The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.

Indexed as

BrainTranscranial Magnetic StimulationHumansNeuroimagingBrain stimulationElectric fieldIndividualizationModelingOptimal placementTranscranial magnetic stimulation

Identifiers

PMID38061463
PMCPMC10922371
OpenAlexW4389331985

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.